Examining the Intellectual Structure of a Medical Informatics Journal with Author Co-citation Analysis and Co-word Analysis

Abstract

Due to the development of science and technology, the convergence of various disciplines has been fostered. Accordingly, interdisciplinary studies have increasingly been expanded by integrating knowledge and methodology from different disciplines. The primary focus of biblimetric methods is on investigating the intellectual structure a field, and analysis of the characterization of interdisciplinary studies is overlooked. In this study, we aim to identify the intellectual structure of the field of medical informatics through author co-citation analysis and co-word analysis by the representative journal "IEEE ENG MED BIOL." In addition, we examine authors and MeSH Terms of top three representative journals for further analysis of the field. We examine the intellectual structure of the medical informatics field by author and word clusters to identify the network structure of medical informatics disciplines.

Intellectual structure evolution of open access research observed through correlation index of keyword centrality

“…Scholars from many disciplines are exploring intellectual structures based on keywords or citation information. Intellectual structures have been analyzed in various fields, such as informatics (Ding et al 1999: Suh and Jeong 2013: Park and Jeong 2013, medicine (Heo and Song 2013), economics and management (Alan and Jack 2008;Kwak and Chung 2012). Those studies used the author co-citation (Kwak and Chung 2012), author bibliographic coupling analysis (Park and Jeong 2013), and co-words analysis (Heo and Song 2013;Chen et al 2016).…”

“…Intellectual structures have been analyzed in various fields, such as informatics (Ding et al 1999: Suh and Jeong 2013: Park and Jeong 2013, medicine (Heo and Song 2013), economics and management (Alan and Jack 2008;Kwak and Chung 2012). Those studies used the author co-citation (Kwak and Chung 2012), author bibliographic coupling analysis (Park and Jeong 2013), and co-words analysis (Heo and Song 2013;Chen et al 2016). In particular, co-words analysis has proven to be an effective technique that reveals the relationship between topics through relationships between keywords (Ronda-Pupo and Guerras-Martin 2012; Rokaya et al 2008).…”